README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: >-
    Nterprise Linux Services is expected to be available before then end of this
    year.
  sentences:
  - >-
    Beta versions of Nterprise Linux Services are expected to be available on
    certain HP ProLiant servers in July.
  - Spain turning back the clock on siestas
  - I don't like many flavored drinks.
- source_sentence: Iran hopes nuclear talks will yield 'roadmap'
  sentences:
  - Iran Nuclear Talks in Geneva Spur High Hopes
  - A black pet dog runs around in the garden of a house.
  - >-
    The witness was a 27-year-old Kosovan parking attendant, who was paid by the
    News of the World, the court heard.
- source_sentence: Hamas Urges Hizbullah to Pull Fighters Out of Syria
  sentences:
  - >-
    "This was a persistent problem which has not been solved, mechanically and
    physically," said board member Steven Wallace.
  - A small dog jumps over a yellow beam.
  - Hamas calls on Hezbollah to pull forces out of Syria
- source_sentence: Licensing revenue slid 21 percent, however, to $107.6 million.
  sentences:
  - Britain loses bid to deport radical cleric Abu Qatada
  - A man sits on a bed very close to a small television.
  - License sales, a key measure of demand, fell 21 percent to $107.6 million.
- source_sentence: >-
    Comcast Class A shares were up 8 cents at $30.50 in morning trading on the
    Nasdaq Stock Market.
  sentences:
  - The stock rose 48 cents to $30 yesterday in Nasdaq Stock Market trading.
  - 'Malaysia: Chinese satellite found object in ocean'
  - A boy in a robe sits in a chair.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: semantic-similarity
      name: 意味的類似性 (Semantic Similarity)
    metrics:
    - type: pearson_cosine
      value: 0.4639747212598005
      name: ピアソン相関係数 (コサイン類似度)
    - type: spearman_cosine
      value: 0.4595105448711385
      name: スピアマン相関係数 (コサイン類似度)
license: gemma
---

# SentenceTransformer

これは、訓練済みの[sentence-transformers](https://www.SBERT.net)モデルです。このモデルは、文と段落を256次元の密なベクトル空間にマッピングし、意味的テキスト類似性、意味検索、言い換えマイニング、テキスト分類、クラスタリングなどに使用できます。

## モデル詳細

![image/png](https://cdn-uploads.huggingface.co/production/uploads/67761d25fb96b78ed6839812/09eHVFfvEDC4ChNzD_n6K.png)

### モデルの説明
- **モデルタイプ:** Sentence Transformer
- **最大シーケンス長:** 2048トークン
- **出力次元数:** 256次元
- **類似度関数:** コサイン類似度

### モデルのソース

- **ドキュメント:** [Sentence Transformers Documentation](https://sbert.net)
- **リポジトリ:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 完全なモデルアーキテクチャ

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 使用方法

### 直接使用 (Sentence Transformers)

まず、Sentence Transformersライブラリをインストールします:

```bash
pip install -U sentence-transformers
```

次に、このモデルをロードして推論を実行できます。
```python
from sentence_transformers import SentenceTransformer

# 🤗 Hubからダウンロード
model = SentenceTransformer("sentence_transformers_model_id")
# 推論を実行
sentences = [
    'Comcast Class A shares were up 8 cents at $30.50 in morning trading on the Nasdaq Stock Market.',
    'The stock rose 48 cents to $30 yesterday in Nasdaq Stock Market trading.',
    'Malaysia: Chinese satellite found object in ocean',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

# 埋め込みベクトルの類似度スコアを取得
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5752, 0.2980],
#         [0.5752, 1.0000, 0.2161],
#         [0.2980, 0.2161, 1.0000]])
```

## 評価

### メトリクス

#### 意味的類似性

* [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)で評価

| メトリクス          | 値         |
|:--------------------|:-----------|
| pearson_cosine      | 0.464      |
| **spearman_cosine** | **0.4595** |

## 訓練詳細

### 訓練データセット

#### 名称未設定のデータセット

* サイズ: 5,749 訓練サンプル
* カラム: `sentence_0`, `sentence_1`, `label`
* 最初の1000サンプルに基づくおおよその統計:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | 型      | string                                                                            | string                                                                            | float                                                          |
  | 詳細    | <ul><li>最小: 6 トークン</li><li>平均: 14.76 トークン</li><li>最大: 55 トークン</li></ul> | <ul><li>最小: 6 トークン</li><li>平均: 14.73 トークン</li><li>最大: 57 トークン</li></ul> | <ul><li>最小: 0.0</li><li>平均: 0.55</li><li>最大: 1.0</li></ul> |
* サンプル:
  | sentence_0                                                                  | sentence_1                                                                           | label                           |
  |:----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------|
  | `Forecasters said warnings might go up for Cuba later Thursday.`            | `Watches or warnings could be issued for eastern Cuba later on Thursday.`            | `0.8`                           |
  | `Death toll in Lebanon bombings rises to 47`                                | `1 suspect arrested after Lebanon car bombings kill 45`                              | `0.5599999904632569`            |
  | `Three dogs running on a racetrack.`                                        | `Three dogs round a bend at a racetrack.`                                            | `0.9600000381469727`            |
* 損失関数: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) 以下のパラメータを使用:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### 訓練ハイパーパラメータ
#### デフォルト以外のハイパーパラメータ

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin

#### すべてのハイパーパラメータ
<details><summary>クリックして展開</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### 訓練ログ
| エポック | ステップ | 訓練損失 | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|
| 1.0    | 360  | -             | 0.2967          |
| 1.3889 | 500  | 0.11          | 0.3338          |
| 2.0    | 720  | -             | 0.3665          |
| 2.7778 | 1000 | 0.0857        | 0.4101          |
| 3.0    | 1080 | -             | 0.4595          |


### フレームワークのバージョン
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0

## 引用

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```